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Topical Presentation of Search Results on Database

  • Hao Hu
  • Mingxi Zhang
  • Zhenying He
  • Peng Wang
  • Wei Wang
  • Chengfei Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8422)

Abstract

Clustering and faceting are two ways of presenting search results in database. Clustering shows the summary of the answer space by grouping similar results. However, clusters are not self-explanatory, thus users cannot clearly identify what can be found inside each cluster. On the other hand, faceting groups results by labelling, but there might be too many facets that overwhelm users.

In this paper, we propose a novel approach, topical presentation, to better present the search results. We reckon that an effective presentation technique should be able to cluster results into reasonable number of groups with intelligible meaning, and provide as much information as possible on the first screen. We define and study the presentation properties first, and then propose efficient algorithms to provide real time presentation. Extensive experiments on real datasets show the effectiveness and efficiency of the proposed method.

Keywords

Time Cost Query Result Character Coverage Answer Space Distinct Tuples 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Agrawal, S., Chaudhuri, S., Das, G., Gionis, A.: Automated ranking of database query results. In: CIDR (2003)Google Scholar
  2. 2.
    Carpineto, C., Osinski, S., Romano, G., Weiss, D.: A survey of web clustering engines. ACM Comput. Surv. 41(3) (2009)Google Scholar
  3. 3.
    Carterette, B., Chandar, P.: Probabilistic models of ranking novel documents for faceted topic retrieval. In: CIKM, pp. 1287–1296 (2009)Google Scholar
  4. 4.
    Chakrabarti, K., Chaudhuri, S., won Hwang, S.: Automatic categorization of query results. In: SIGMOD, pp. 755–766 (2004)Google Scholar
  5. 5.
    Chaudhuri, S., Das, G., Hristidis, V., Weikum, G.: Probabilistic information retrieval approach for ranking of database query results. ACM TODS 31(3), 1134–1168 (2006)CrossRefGoogle Scholar
  6. 6.
    Dakka, W., Ipeirotis, P.G., Wood, K.R.: Automatic construction of multifaceted browsing interfaces. In: CIKM, pp. 768–775 (2005)Google Scholar
  7. 7.
    Demidova, E., Fankhauser, P., Zhou, X., Nejdl, W.: DivQ: Diversification for keyword search over structured databases. In: SIGIR, pp. 331–338 (2010)Google Scholar
  8. 8.
    Donohue, J.C.: Understanding scientific literatures: A Bibliometric Approach. The MIT Press, Cambridge (1973)Google Scholar
  9. 9.
    Hochbaum, D.S. (ed.): Approximation algorithms for NP-hard problems. PWS Publishing Co., Boston (1997)Google Scholar
  10. 10.
    Hu, H., Zhang, M., He, Z., Wang, P., Wang, W.: Diversifying query suggestions by using topics from wikipedia. In: Web Intelligence, pp. 139–146 (2013)Google Scholar
  11. 11.
    Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: ICML, pp. 170–178 (1997)Google Scholar
  12. 12.
    Li, C., Yan, N., Roy, S.B., Lisham, L., Das, G.: Facetedpedia: Dynamic generation of query-dependent faceted interfaces for wikipedia. In: WWW, pp. 651–660 (2010)Google Scholar
  13. 13.
    Liu, B., Jagadish, H.V.: Using trees to depict a forest. PVLDB 2(1), 133–144 (2009)Google Scholar
  14. 14.
    Luo, Y., Lin, X., Wang, W., Zhou, X.: Spark: Top-k keyword query in relational databases. In: SIGMOD, pp. 115–126 (2007)Google Scholar
  15. 15.
    Luo, Y., Wang, W., Lin, X., Zhou, X., Wang, J., Li, K.: Spark2: Top-k keyword query in relational databases. IEEE Trans. Knowl. Data Eng. 23(12), 1763–1780 (2011)CrossRefGoogle Scholar
  16. 16.
    Roy, S.B., Wang, H., Das, G., Nambiar, U., Mohania, M.K.: Minimum-effort driven dynamic faceted search in structured databases. In: CIKM, pp. 13–22 (2008)Google Scholar
  17. 17.
    Scaiella, U., Ferragina, P., Marino, A., Ciaramita, M.: Topical clustering of search results. In: WSDM, pp. 223–232 (2012)Google Scholar
  18. 18.
    Singh, M., Nandi, A., Jagadish, H.V.: Skimmer: Rapid scrolling of relational query results. In: SIGMOD Conference, pp. 181–192 (2012)Google Scholar
  19. 19.
    Wu, T., Li, X., Xin, D., Han, J., Lee, J., Redder, R.: Datascope: Viewing database contents in google maps’ way. In: VLDB, pp. 1314–1317 (2007)Google Scholar
  20. 20.
    Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., Ma, J.: Learning to cluster web search results. In: SIGIR, pp. 210–217 (2004)Google Scholar
  21. 21.
    Zhao, F., Zhang, X., Tung, A.K.H., Chen, G.: Broad: Diversified keyword search in databases. PVLDB 4(12), 1355–1358 (2011)Google Scholar
  22. 22.
    Zhou, B., Pei, J.: Answering aggregate keyword queries on relational databases using minimal group-bys. In: EDBT, pp. 108–119 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hao Hu
    • 1
    • 2
  • Mingxi Zhang
    • 1
    • 2
  • Zhenying He
    • 1
    • 2
  • Peng Wang
    • 1
    • 2
  • Wei Wang
    • 1
    • 2
  • Chengfei Liu
    • 3
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Data ScienceFudan UniversityChina
  3. 3.Faculty of ICTSwinburne University of TechnologyMelbourneAustralia

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